ANALYTICS

Cluster

I want to automatically identify clusters of points in the data

Cluster analysis is a family of algorithms designed to form groups such that the group members are more similar versus non-group members. Clustering is used for analyzing data which does not include pre-labeled classes, or even a class attribute at all. Data instances are grouped together using the concept of maximizing the intra-class similarity and minimizing the inter-class similarity.

K-means

K points are randomly chosen as cluster centers, or centroids, and all training instances are plotted and added to the closest cluster. After all instances have been added to clusters, the centroids, representing the mean of the instances of each cluster are re-calculated, with these re-calculated centroids becoming the new centers of their respective clusters. This iterative process continues until there is no change to the centroids or their membership, and the clusters are considered settled.

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Load Data

WHO Covid global dataset as of September 24th 2021 at 10.46.22 AM

# Fold hide as this is not relevant to the task

df<-read_csv('archetypes/WHO COVID-19 global table data September 24th 2021 at 10.46.22 AM.csv')
df

Data Wrangling

Select columns

df1 <- df%>%select(c(1,2,4,6,9,11))%>%filter(Name!='Global')

df1

Checking missing values

missing_stats <- purrr::map_df(df1, ~ sum(is.na(.))) %>%
  gather('Column name', 'Count of missing values')

missing_stats

df2<-na.omit(df1)
df2

Scale numeric columns

df_scaled<-df2
df_scaled[,3:6] <- scale(df2[,3:6])
df_scaled

Use Elbow Method to find the ideal number of clusters

#if(!require(devtools)) install.packages("devtools")
#devtools::install_github("kassambara/factoextra")

library(factoextra)
### Elbow method (look at the knee)

v1<-fviz_nbclust(df_scaled[,3:6], kmeans, method = "wss") +
geom_vline(xintercept = 3, linetype = 2)
girafe(ggobj = v1, width_svg = 13, height_svg = 7,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

Run kmeans model

library(stats)

set.seed(123)

clusters <- kmeans(df_scaled[,3:6], 3, iter.max = 20, nstart = 25)


df2$cluster <- as.factor(clusters$cluster)

df2

Plot the clusters

# Plot the clusters
v2 <- fviz_cluster(clusters, geom="point", data=df2[,3:6],palette = "Set2") + 
  ggtitle("k = 3") + 
  theme_minimal()
girafe(ggobj = v2, width_svg = 13, height_svg = 7,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

Which group is still in high risk of covid?

# Calculate means for each cluster
df_mean <- df2 %>%
    group_by(cluster) %>%
    summarise(n = n(),
        `mean_Cases - cumulative total per 100000 population` = mean(`Cases - cumulative total per 100000 population`),
        `mean_Cases - newly reported in last 7 days per 100000 population` = mean(`Cases - newly reported in last 7 days per 100000 population`),
        `mean_Deaths - cumulative total per 100000 population` = mean(`Deaths - cumulative total per 100000 population`),
        `mean_Deaths - newly reported in last 7 days per 100000 population` = mean(`Deaths - newly reported in last 7 days per 100000 population`),
       )

df_mean

Group 3

df_group3<-df2%>%filter(cluster==3)
df_group3

Group 1: safe group?

df_group1<-df2%>%filter(cluster==1)
df_group1

References

The citations and data sources used for this case

• WHO Coronavirus (COVID-19) Dashboard
• K-means Cluster Analysis

Use

This work is licensed under
Attribution 4.0 International
CC BY 4.0

Source

Creation

Hand crafted with ♥ by
the Kamino Crew